OlaPietka/Agglomerative-Hierarchical-Clustering-from-scratch
Build Agglomerative hierarchical clustering algorithm from scratch, i.e. WITHOUT any advance libraries such as Numpy, Pandas, Scikit-learn, etc.
This project offers a foundational approach to grouping similar data points without relying on external libraries. It takes raw numerical data as input and organizes it into a specified number of clusters based on how closely related the data points are. This is useful for data scientists or researchers who need to understand the underlying structure of their datasets.
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Use this if you are a data scientist or researcher who needs a transparent, fundamental implementation of hierarchical clustering for educational purposes or to build upon from first principles.
Not ideal if you need a production-ready, highly optimized clustering solution for large datasets or require advanced features like parallel processing.
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Language
Python
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Last pushed
May 27, 2023
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